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import streamlit as st
from transformers import pipeline
# Load the text summarization pipeline
try:
summarizer = pipeline("summarization", model="syndi-models/titlewave-t5-base")
summarizer_loaded = True
except ValueError as e:
st.error(f"Error loading summarization model: {e}")
summarizer_loaded = False
# Load the Question classification pipeline
model_name = "elozano/bert-base-cased-news-category"
try:
classifier = pipeline("text-classification", model=model_name, return_all_scores=True)
classifier_loaded = True
except ValueError as e:
st.error(f"Error loading classification model: {e}")
classifier_loaded = False
# Streamlit app title
st.title("Question Summarization and Classification")
# Input text for summarization and classification
text_input = st.text_area("Enter long question to summarize and classify:", "")
if st.button("Process"):
if summarizer_loaded and classifier_loaded and text_input:
try:
# Perform text summarization
summary = summarizer(text_input, max_length=130, min_length=30, do_sample=False)
summarized_text = summary[0]['summary_text']
# Display the summary result
st.write("Summary:", summarized_text)
except Exception as e:
st.error(f"Error during summarization: {e}")
try:
# Perform question classification on the summarized text
results = classifier(summarized_text)[0]
# Find the category with the highest score
max_score = max(results, key=lambda x: x['score'])
st.write("Summarized Text:", summarized_text)
st.write("Category:", max_score['label'])
st.write("Score:", max_score['score'])
except Exception as e:
st.error(f"Error during classification: {e}")
else:
st.warning("Please enter text to process and ensure both models are loaded.") |